Method, System and Software Product for the Measurement of Heart Rate Variability

ABSTRACT

The invention relates to a method, system, and software product for measuring heart rate variability. The method comprises instructing a user when to breathe in and breathe out, receiving a signal from a sensor responsive to the heart beat of the user, processing the received signal to determine heart beat intervals of the user and calculating a measure of heart rate variability of the user from the processed heart beat intervals.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.12/565,717, filed Sep. 23, 2009, and entitled Method, System andSoftware Product for the Measurement of Heart Rate Variability, nowpending, which claims the benefit of U.S. Provisional Patent ApplicationNo. 61/143,265, filed Jan. 8, 2009, and entitled Method for ConvenientDaily Measurement of Heart Rate Variability. The disclosure of eachpatent application identified above is incorporated herein by referencein its entirety.

COPYRIGHT NOTICE AND AUTHORIZATION

Portions of the documentation in this patent document contain materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice file or records, but otherwise reserves all copyright rightswhatsoever.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is in the technical field of cardiac and autonomicnervous system function monitoring in human beings. More particularly,the present invention is in the technical field of Heart RateVariability (HRV) measurements in human daily life.

2. Description of the Related Art

Heart Rate Variability (HRV) is based on the measurement of the timedifference between each heartbeat, i.e., the beat-to-beat variabilitywhen processed using time domain, frequency domain or other measures, asdescribed in “Heart rate variability: Standards of measurement,physiological interpretation, and clinical use”, published 1996, TaskForce of The European Society of Cardiology and The North AmericanSociety of Pacing and Electrophysiology, hereinafter referred to as“Task Force”, and elsewhere. Each R-wave (of the P-Q-R-S-T sequence inECG terminology) represents a forceful contraction of the heart andcreates the pulse. The time difference between beats is known as the R-Rinterval. The beat-to-beat variability is affected by stimulation fromthe autonomic nervous system on the heart. The autonomic nervous systemhas two branches that usually work in an antagonistic manner: thesympathetic nervous system (SNS) that stimulates internal organs inpreparation for “fight or flight” behavior, and the parasympatheticnervous system (PNS), that is associated with relaxation and allowshumans to “rest and digest”. The higher the level of PNS activity, thegreater the freedom from physiological and psychological stress theperson is experiencing. A good way to observe PNS activitynon-invasively is to examine changes in heart rate in response tobreathing. Heart rate increases during inhalation and decreases duringexpiration. This effect is known as respiratory sinus arrhythmia (RSA),and the changes in heart rate depend on breathing rate and depth (amongother variables). High levels of PNS activity (as measured via RSA)indicate that the person is exhibiting a freedom from physiological andpsychological stress. High parasympathetic HRV is known to becardioprotective in the sense that persons with consistently highmeasurements of this parameter are less likely to suffer potentiallyfatal cardiac arrhythmia than persons with lower values. This principlemay be used to measure relative changes in the effect of physical ormental stressors on an individual when their HRV is measured on afrequent (e.g., daily) basis, as described below in relation to oneaspect of the invention.

The analysis and use of heart rate variability measurements has beenestablished for more than 40 years, and has proven to have diagnosticand prognostic value in many clinical applications including, but notlimited to: state of recovery following acute heart attack, work relatedstress level assessment, and fetal distress. Heart rate variability hasalso been used retrospectively to examine the impact of exercise andtraining that have already been performed by athletes, sedentary usersand patients with existing cardiovascular disease.

At the current time, HRV is conventionally measured according to theGuidelines stated by the Task Force, by capturing ECG data gathered overperiods ranging from 5 mins to 24 hrs using a clinical ECG apparatus or,for longer periods, a Holter recorder. The captured signals are firstdigitized and then processed using a frequency domain transformation inorder to separate the markers of the two separate branches of theautonomic nervous system. In particular, the so called “High Frequency”band (defined as 0.15 Hz to 0.4 Hz) is associated with theparasympathetic (vagal) branch of the autonomic nervous system. The TaskForce recommends short-term recordings of 5 min made underphysiologically stable conditions processed by frequency-domain methods.A frequency domain transformation (e.g., Fast Fourier Transform) isrecommended by the Task Force to have at least 512 data points in orderto allow the accurate determination of power within the High Frequencyband. This means in practice gathering approximately 6-9 mins of datafor persons with resting pulse rates in the range 60-80 bpm.Interpolation of a lower number of data samples (e.g., 400) may also beused, but this still results in practical measurements of 5 minutes.

The present invention relies on the following principles:

1. Correlation between i) the current state of cardiovascular fitnessand recovery in an individual, and ii) activity level of theparasympathetic (vagal) branch of the autonomic nervous system, observedconventionally via power level of HF modulation (HF power) in afrequency domain transformation of heart beat interval (R-R) data underconsistent environmental and physiological conditions.

2. Mathematically proven, and empirically confirmed, relationshipbetween time domain measured RMSSD and the more often used frequencydomain measured HF power, recommended by the Task Force. RMSSD (ms), isthe Root Mean Square Successive Difference, i.e., the square root of themean of the sum of the squares of differences between adjacent normal RRintervals from short-term recordings or from an entire 24 hourelectrocardiogram recording. This time-domain measure strongly reflectsPNS modulation, and has also been shown to be mathematically equivalentto the Poincare SD1 measure times 1.414. The RMSSD measure has mainlybeen replaced in studies during the last 10 years by frequency domainanalyses, since the latter give a more complete insight from the R-Rinterval data into the workings of multiple branches of the ANS.Nonetheless, the RMSSD time domain measure can give faster, accurateresults when only the activity of the PNS is desired to be known.

3. High degree of correlation, and predictive accuracy, of ultra shortterm (e.g., 30 sec) versus more usual short term (5 min or greater)RMSSD measurements. Reference: “Accuracy of ultra short heart ratevariability measurements”, Engineering in Medicine and Biology Society,2003, Proceedings of the 25th Annual International Conference of theIEEE. For example, the Task Force recommends short term RMSSDmeasurements.

4. Use of low frequency (typically <0.15 Hz), controlled deep breathingtechniques during the measurement period in order to reduce the knownimpact of respiration rate and depth on HRV measurements, and also tohelp the user to relax, focusing the measurement result on physical(rather than mental) stress changes.

5. When possible, use of a static, standing position for measurementtaking, avoiding supine saturation effects that can otherwise reduce therange of observed HRV measurements, especially in athletic individuals.

6. Recommendation to the user to take the daily measurement at about thesame time each day, to further reduce variability, in this case causedby time of day induced (circadian) HRV variation.

SUMMARY OF THE INVENTION

The invention provides a method, system and computer software productfor measuring heart rate variability of a user. As a first aspect of theinvention, the invention provides a method of measuring heart ratevariability of a user comprising instructing the user when to breathe inand breathe out, receiving a signal from a sensor responsive to theheart beat of the user, processing the received signal to determineheart beat intervals of the user and calculating a measure of heart ratevariability of the user from the processed heart beat intervals.

As a second aspect of the invention, the invention provides a method ofmeasuring heart rate variability of a user comprising instructing theuser to breathe in and breathe out at predetermined times, receiving asignal from a sensor responsive to the heart beat of the user,processing the received signal to determine heart beat intervals andcalculating a measure of heart rate variability from the processed heartbeat intervals using a time domain processing method.

As a third aspect of the invention, the invention provides a method ofawakening a sleeping user based on measuring heart rate variability,comprising receiving a signal from a sensor responsive to the heart beatof the user, processing the received signal to determine heart beatintervals, calculating a first measure of heart rate variability of theuser from the processed heart beat intervals while the user is awake,calculating a second measure of heart rate variability of the user fromthe processed heart beat intervals while the user is asleep, computingthe difference between the second measure and the first measure andgenerating an awakening stimulus to the user when the difference exceedsa predetermined threshold.

As a fourth aspect of the invention, the invention provides a system formeasuring heart rate variability of a user, comprising means forinstructing the user when to breathe in and breathe out, means forreceiving a signal from a sensor responsive to the heart beat of theuser, means for processing the received signal to determine heart beatintervals of the user and means for calculating a measure of heart ratevariability of the user from the processed heart beat intervals.

As a fifth aspect of the invention, the invention provides a computersoftware product comprising coded instructions for executing a computerprocess in a digital processor, which computer process generates ameasure of heart rate variability. The computer process comprisesmanaging instructing a user when to breathe in and breathe out,inputting processed heart beat intervals, wherein the processed heartbeat intervals are output by a signal processing means and the input ofthe signal processing means comprises a signal received by a receivingmeans from a sensor responsive to the heart beat of the user, andcalculating a measure of heart rate variability of the user fromprocessed heart beat intervals.

As a sixth aspect of the invention, the invention provides a computersoftware product comprising coded instructions for executing a computerprocess in a digital processor, which computer process managesgenerating an awakening stimulus to a sleeping user by managing themeasurement of heart rate variability. The computer process comprisesinputting processed heart beat intervals, wherein the processed heartbeat intervals are output by a signal processing means and the input ofthe signal processing means comprises a signal received by a receivingmeans from a sensor responsive to the heart beat of the user,calculating a first measure of heart rate variability of the user fromthe processed heart beat intervals while the user is awake, calculatinga second measure of heart rate variability of the user from theprocessed heart beat intervals while the user is asleep, computing thedifference between the second measure and the first measure and managinggenerating an awakening stimulus to the user when the difference exceedsa predetermined threshold.

The present invention comprises an improved method and system fortaking, processing and presenting HRV measurements. The presentinvention captures signals generated by electrocardiographic (forexample, chest strap ECG or other sensor of heart rate activity) orblood pressure (finger, earlobe, wrist or other) sensors in order togenerate a heart rate variability index (for example, RMSSD or othertime domain measure) more quickly, with higher repeatability and lessneed for training or expert instruction than previous methods.

The present invention collects, processes and displays HRV readingsderived from electrocardiographic sensor data over a variable periodwhile the user wears one of a plurality of sensors and breathes in adeep and controlled manner.

The present invention embodies the following aspects:

1. Use of very short term (1 min or less) versus more usual longer term(5 min or greater) HRV measurements

2. Use of low frequency (<0.15 Hz), controlled deep breathing techniquesduring the measurement period in order to reduce the impact ofrespiration rate and depth on HRV measurements. The combination of pacedbreathing and RMSSD can achieve reliable results in a short time.

3. Suggestion to the user via a number of possible methods including onscreen display instructions or separate reference card, printedinstructions or automated alarm clock function within the portabledevice itself to take the measurement at about the same time each day,to further reduce variability, in this case caused by time of day(circadian) induced HRV variation.

The above aspects provide an improved level of repeatability andconvenience for periodic HRV measurements compared to current HRVmeasurement techniques, in particular the ability to use a mobile deviceto obtain an almost instant, reliable and repeatable reading (forexample, 1 min is much shorter than conventional measurements taking atleast 5 mins and possibly much longer), which can then be comparedagainst measurements taken days, weeks, months or even years previouslyin order to gain insight on short, medium and long term HRV trends andfulfill a need for personal longitudinal data logging.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description will be better understood when readin conjunction with the appended drawings, in which there is shown oneor more of the multiple embodiments of the present invention. It shouldbe understood, however, that the various embodiments of the presentinvention are not limited to the precise arrangements andinstrumentalities shown in the drawings.

In the Drawings:

FIG. 1 is an exemplary overview of the method and system for HRVmeasurement;

FIG. 2 is an exemplary diagrammatic view of the series of signalprocessing steps of one embodiment;

FIG. 3 shows an exemplary decomposition of the system into functionalunits;

FIG. 4 is an exemplary flow chart of one aspect of the heart rateprocessing;

FIG. 5 is an exemplary pictorial representation of the user interfacewhile the HRV measurement is taking place and at its completion;

FIG. 6 is an exemplary pictorial representation of the user interfacefor presenting results of the HRV measurements;

FIG. 7 shows an example of the steps of the computation and display oftraining load and HRV results;

FIG. 8 shows an example of the steps of the computation and display oftraining recommendations based on HRV measurements;

FIG. 9 is an exemplary pictorial representation of a portion of the userinterface for displaying HRV and mood information.

FIG. 10 shows an example of the steps of determining and presentingrecommendations regarding the management of hypertension based on HRVmeasurements;

FIG. 11 shows an example of the steps of determining and presentingrecommendations relevant to overtraining based on HRV measurements;

FIG. 12 shows an example of the steps determining and presentingexercise recommendations to users suffering from heart failure based onHRV measurements;

FIG. 13 shows an example of the steps of monitoring and alerting a userto a potentially serious decline in parasympathetic activity based onHRV measurements.

DETAILED DESCRIPTION

Referring now to FIG. 1, there is shown one general exemplary functionalembodiment of a system 100 for measuring heart rate variability of auser 102. The user 102 may put on a sensor 104, for example, acommercial chest ECG sensor strap. An animation 106 that indicates tothe user 102 when to breathe in and breathe out may be shown on displayfunction 108, for example, an LCD display forming part of, for example,a handheld portable device 110. Animation 106 may be displayed byhardware and/or software known in the art, for example displaygeneration software and hardware within device 110. Alternatively,display function 108 may be realized by any suitable type of displaythat comprises part of a portable or non-portable device, or it may be aself-contained display device connected by wire or wirelessly to device110 (not shown in FIG. 1). The signal from sensor 104 may be processedby receiver function 112, for example, to demodulate the sensor signaland convert it to a digital format. Receiver function 112 may berealized in hardware and/or software known in the art, for example, areceiving antenna in hardware followed by A/D conversion, filtering anddemodulation in software and/or hardware. The output of receiverfunction 112 may be processed by signal processor function 113, forexample to reliably generate heart beat intervals for HRV calculation byHRV calculator 114. Signal processor function 113 may be realized inhardware and/or software, for example, by a counter/timer function insoftware and/or hardware. HRV calculator 114 may be realized in hardwareand/or software, for example, by software running on a digital CPU,special purpose digital hardware, or a combination of software andhardware.

In one embodiment of the present invention, animation 106 may indicateto the user to breathe in for a count of S1 (for example, S1=3) secondsand out for a count of S2 (for example, S2=5) seconds, with thissequence to be repeated 4 or more times within a measurement timewindow.

In some embodiments, a first sound, for example, the sound of advancingocean waves, may be generated, for example by device 110 to indicate tothe user 102 when to breathe in, and a second sound, for example thesound of retreating ocean waves, may be generated by hardware and/orsoftware known in the art, for example, by software running on a digitalCPU, special purpose hardware, or a combination of software andhardware, within device 110 to indicate to the user 102 when to breatheout. Alternatively, these sounds may be generated by external functionsconnected to device 110 (not shown in FIG. 1).

In some embodiments, sensor 104 may be closely attached to or withindevice 110. In these embodiments, device 110 may, for example, be placedon the skin or clothing of the user 102 by the user or anotherindividual, for example, a medical technician.

In some embodiments, the signal from sensor 104 may be connected by wireto device 110. Alternatively, the signal from sensor 104 may beconnected wirelessly to device 110; in these embodiments, receiverfunction 112, signal processor function 113 and HRV calculator function114 may comprise part of device 110. Alternatively, receiver function112 and/or signal processor function 113 and/or HRV calculator function114 may be external to device 110. Receiver function 112 and/or signalprocessor function 113 and/or HRV calculator function 114 may, forexample, be realized by one or more physical modules that plug in todevice 110, using connectors and signaling protocols known in the art.Functions 112-114 may all be realized in one physical module or in manymodules, e.g., FIG. 1 should not be understood to imply a one-to-onemapping between functions and physical modules.

In some embodiments, for example in embodiments where one or more offunctions 112-114 are external to device 110, analog and/or digitalconnections, of types known in the art, shown by dashed lines in FIG. 1,may convey signals back and forth among the subset of functions 112-114that are external to device 110 and among each other, as appropriate toexchange the information necessary to carry out the functions ofprocessing stages 112-114. The connections represented by dashed linesin FIG. 1 may be realized using any number of wired conductors orwireless links, and may employ multiplexing techniques to minimize thenumber of such conductors and/or links, as known in the art. Dashedlines are shown as bidirectional in FIG. 1 to include the exchange ofcontrol and/or intermediate results of processing calculations, asappropriate, between processing stages 112-114 and device 110, however,the basic heart rate information processing sequence is from sensor 104to receiver function 112 to signal processor function 113 to HRVcalculator function 114. In some embodiments where both the signalprocessor function 113 and the HRV calculator function 114 are externalto device 110, the output of signal processor function 113 may beconnected to both the HRV calculator function 114 and directly to device110, for example, so that processed heart beat intervals may be conveyedto device 110 as well as to HRV calculator function 114.

In some embodiments, synthesized audio may instruct the user 102, forexample, on how to place the sensor 104, and/or when to breathe in andbreathe out, and/or when the measurement is complete. In someembodiments, the synthesized audio may also deliver the results ofmeasurements and/or computations to the user 102. Synthesized audio maybe generated by hardware and/or software known in the art, for example,by a combination of software running on a digital CPU and D/A conversionhardware within device 110. Alternatively, these sounds may be generatedby external functions connected to device 110 (not shown in FIG. 1).

In some embodiments, audible commands and/or responses may be receivedfrom the user 102. For example, the user 102 may say “start” to causethe system to begin a HRV measurement. Audible input may be recognizedby hardware and/or software known in the art, for example, by acombination of A/D conversion hardware and software running on a digitalCPU and within device 110. Alternatively, these sounds may be receivedand/or recognized by external functions connected to device 110 (notshown in FIG. 1).

Referring now to FIG. 2, in one exemplary embodiment, there is shown thefrontal view of a user 202, who is in a static, standing up(orthostatic) position preferred for taking a heart rate variabilitymeasurement. The user 202 may first put on either a commercial chest ECGsensor strap 204 (not part of the present invention) which may use, forexample, 5.3 kHz amplitude modulated inductive transmission techniquesto send ECG signals to the resonant antenna 208. Due to the use ofinductive transmission, the resonant antenna 208 has to be in closeproximity (<1 m) to the chest strap sensor 204. Alternatively acommercial ECG, wrist, ear or finger cuff blood pressure sensor 206 maybe connected, for example via a cable (not shown in FIG. 2), to theamplifier 210.

The received signal in antenna 208 may consist of bursts of 5.3 kHzamplitude modulation at a level of a few millivolts (mV), so theinductor and capacitor values should be appropriately chosen to beresonant at this frequency. Alternatively, just an inductor, instead ofthe resonant circuit 208, may be used, but the antenna efficiency andtherefore useful working range of the transmission will be reduced. Thereceived signal may be amplified by high gain amplifier 210, beforebeing converted to the digital domain by analog to digital converter212. The latter should have dynamic range of at least 8 bits, and samplerate at least twice the received signal bandwidth to avoid aliasing(Nyquist criterion). In one embodiment, processing stages 210 and 212are provided by the microphone input of, for example, the mobile device228.

Once digitized, the signal, for example in case of origination from acommercial ECG chest strap, may be bandpass filtered with centerfrequency of 5.3 kHz in filter 214. Since the received signal isamplitude modulated, the envelope of the signal may be recovered bydemodulator 216. This may be achieved, for example, by conventional fullwave rectification and low pass filtering. The successive rising edgesof the resulting demodulated signal may then be used to start and stop acounter-timer 218, which according to Task Force guidelines should havemeasurement precision better than 2 ms. In order to prevent falsetriggering from noise signals, ectopic beats, or non-ECG coding signalsin the chest strap transmission, the counter-timer 218 may run for aminimum of 500 ms before it is allowed to be reset. This exemplary valueis chosen under the expectation that a user 202 taking readings in thepreferred stationary standing position would have a heart rate lowerthan 120 beats per minute. In one embodiment, processing stages 214-218are performed by hardware and/or software modules within mobile device228.

The output from counter-timer 218 is a series of consecutive R-RInterval (RRI) values, measured in milliseconds (ms), with a new valuebeing sent to statistical processor 220 about once per second,equivalent to the heart rate of the user 202, normally around 60 beatsper minute. In statistical processor 220, in one exemplary embodiment,the Root Mean Squared Successive Difference (RMSSD) may be calculatedaccording to a conventional formula:

${R\; M\; S\; S\; D} = \sqrt{\frac{\sum\limits_{1}^{n}\left( {{R\; R\; I_{n}} - {R\; R\; I_{n - 1}}} \right)^{2}}{\left( {n - 1} \right)}}$

as shown in block 220 of FIG. 2. This process involves first squaringthe adjacent R-R Interval (RRI) differences, and incrementing the valueof n each time a new sample is received from counter-timer 218.Elimination of irregular beat data may be achieved, for example, bylooking for any RRI squared values greater than 40000, which wouldindicate a RRI to RRI difference of more than 200 ms, which is veryunlikely to occur in normal sinus heart rhythm. Any value greater than40000 is therefore not added to the running total sum; neither is the nvalue of the divisor in statistical processor 220 incremented in thatcase. Subject to this exception, the resulting RMSSD value may beupdated every time a new RRI value is received from counter-timer 218,and displayed in both textual and graphic form on the display 226 of theportable device 228. Values of RRI and RMSSD may be stored in database222. In one embodiment, processing stages 218-222 are performed byhardware and/or software modules within mobile device 228.

Alternatives to RMSSD are pNN50, the probability of any NN(normal-to-normal RR after exclusion of irregular beats) intervaldifference exceeding 50 ms, and the SD1 parameter of the clusterPoincare plot (2D plot of adjacent R-R intervals), as described in theTask Force paper references for time domain heart rate variabilitymeasures.

In the embodiment of FIG. 2, processing stages 208-216 may represent anexample of a more detailed functional representation of the receiverfunction 112, processing stage 218 may represent an example of a moredetailed functional representation of the signal processor function 113,and processing stages 220-222 may represent an example of a moredetailed functional representation of the HRV calculator 114. Any subsetof functions 208-222 may be implemented internally to device 228.Alternatively, any subset of functions 208-222 may be implementedexternally to device 228. In one embodiment, antenna 208 may be externalto device 228 while functions 210-222 may be internal to device 228.

Referring now to FIG. 3, there is shown one exemplary functionalembodiment of the system for measuring heart rate variability. Heartrate related signals may be received from a sensor, for example, sensor204, via an antenna 302. These signals may be amplified by an amplifier(not shown in FIG. 3), for example, amplifier 210, before beingconverted to digital form by an analog-to-digital converter (A/D) 304,which may comprise the A/D converter 212 in some embodiments.Alternatively, A/D converter 304 may receive heart rate related signalsfrom a sensor, for example sensor 206, via connection 306, which maycomprise, for example a cable. In some embodiments, connection 306 maybe bisected by amplification and/or filtering (not shown in FIG. 3)between the sensor end of connection 306 and the A/D converter 304 endof connection 306. A/D converter 304 may also convert analog audiosignals from an audio transducer, for example, microphone 308, forexample in embodiments where the user interface includes speechrecognition capability. The digitized sensor signals may be received bycentral processor (CPU) 310. CPU 310 may be controlled by instructionsstored in a portion of the address space of memory 312. In someembodiments, CPU 310 may further process the digitized sensor signals,for example, by filtering as described previously with reference toblock 214, and by demodulating as described previously with reference toblock 216. In some embodiments, CPU 310 may detect heart rate intervalsby implementing a counter/timer-type function, for example, as describedpreviously with reference to block 218. CPU 310 may perform heart ratevariability calculations, for example the RMSSD calculations asdescribed previously with reference to block 220. CPU 310 may storeand/or recall intermediate and/or final results of the heart ratevariability processing using memory 312. CPU 310 may, for example,prompt for user input and/or generate instructions to the user and/ordisplay results to the user using display 314, which may comprisedisplay 226 or 108 in some embodiments. CPU 310 may receive user inputvia control panel 316, which may comprise control panel 224 in someembodiments. In some embodiments, CPU 310 may generate sounds, forexample to indicate to the user when to breathe in and breathe out, aspreviously described, using digital-to-analog (D/A) converter 318 andspeaker 320. Alternatively, sounds may be provided to earphones 322.Instructions to the user may also be given, for example by softwarerunning on CPU 310 by retrieving digitized speech from memory 312 andgenerating analog speech sounds using D/A 322 and speaker 320.

It is important when processing heart rate variability signalmeasurements to be able to detect irregular R-R intervals caused bymultiple sources, including ectopic beats, missing beats originatingfrom the heart, and transmission errors between, for example, the cheststrap 204 and the wireless receiver 208. Such irregular R-R intervalscan cause significant errors and distortion in the final calculationresult, leading to incorrect decisions. A series of R-R intervalsprocessed to remove irregularities is known as an N-N interval series.The gold standard for such processing includes a review of the data byan experienced electrocardiographic (ECG) technician.

In some embodiments, the present invention includes a method forautomating the periodic measurement of HRV, and thus in theseembodiments there is no opportunity for manual review of the measuredR-R intervals. This necessitates the inclusion of more sophisticatedmeans of detection of R-R intervals that are likely to be invalid. Thisprocess is assisted by the fact that the current movement status(standing still) and breath state (i.e., exhaling or inhaling following,for example, a lungs animation 106) of the user is known.

In some embodiments, irregular R-R intervals may be excluded from theHRV calculations, e.g., the RMSSD calculations of processor 220,according to the method illustrated in FIG. 4 In FIG. 4, at steps 402and 404 if at any time the measured R-R interval is either shorter than500 ms, or longer than 1800 ms that R-R interval may be excluded at step414 on the basis that it is not a reasonable value for a human being inthe standing position. Irregular intervals may be excluded by hardwareand/or software, for example, by software running on digital CPU 310and/or special purpose counter/timer hardware, for example a hardwarerealization of function 218.

During inhalation, as identified at step 406, the autonomic nervoussystem (parasympathetic branch) of the user withdraws stimulation, withthe consequence that the R-R interval is shortened. Since the method,for example as previously described with reference to FIGS. 1 and 2,includes a controlled breathing pattern, beats that are longer than theaverage R-R interval taken (which may be calculated over one or morecomplete breath cycles) during inhalation may be identified at step 410and excluded at step 414. In practice an additional margin, shown as xin step 410, may be included to allow for the fact that the user'sbreathing may not be perfectly synchronized with, e.g., the lunganimation 106 of FIG. 1.

During exhalation, as identified as not-inhalation at step 406, amaximum allowed difference between adjacent R-R intervals can also bepredicted by knowing the average R-R interval taken over one or morecomplete breath cycles during the measurement. This is possible becausethe parasympathetic-sympathetic balance also controls the standing pulserate. A formula has been determined empirically from observations onmultiple users, whereby the maximum permitted R-R interval differencemay be related to the square of the mean R-R interval (measured inmilliseconds) divided by a constant, for example 12000. Therefore, R-Rintervals not conforming to:

Max allowed adjacent R-R interval difference=(mean R-R interval)²/12000

may be identified at step 412 and excluded at step 414. In someembodiments, the measurement sequence may include preliminary breathingcycles, during which the mean R-R interval is calculated, before the HRVcalculation begins, so that non-valid R-R intervals may be excludedright from the start. Intervals not identified for exclusion accordingto the preceding criteria are included in the HRV calculations, e.g.,the RMSSD calculations of processor 220, at step 416.

In the case that a user has a temporarily irregular heart rhythm, suchas palpitations, Atrial Fibrillation, or SupraVentricular Tachycardia,this may lead to a number of excluded intervals according to the set ofrules outlined in the previous paragraphs. In case the number of suchexcluded beats exceeds a predefined threshold, e.g., 5, during themeasurement sequence, then the measurement will be terminated and theuser informed of the reason, and instructed to retake the measurementafter some period of time. This is in order to prevent incorrectreadings from contaminating the calculation of longitudinal averages andother statistical measures upon which recommendations may be based.

Referring again to FIG. 2 and now to the interaction between theinvention and the user 202, the measurement may first be initiated usingthe controls 224, which may be buttons, touch screen or other suitablemethod provided, for example, on portable device 228 in FIG. 2. FIG. 5shows the user interface displayed on a mobile phone, or other personaldevice with similar processing and display capabilities, during and atthe conclusion of the measurement. During an HRV measurement, indicator502 may prompt the user to breathe in and then out in a controlledmanner and for predefined time periods in each breathing phase using agraphical animation (e.g., a pair of expanding lungs 106 as shown inFIG. 1), or using sounds or other stimuli as described in earlierparagraphs. The overall breathing frequency should be low enough not tosignificantly alter the amplitude of the HRV measurements.

Heart shaped indicator 504 may be displayed on display 226 and pulseaccording to the output of demodulator 216 in FIG. 2, to show the user202 that a valid heart rate signal is being received from sensor 204 or206. Potential problems include a weak signal caused by excessivedistance between sensor 204 or 206 and receiver coil 208, or poorplacement or contact of the sensor 204 or 206. In this case, the user202 may be informed by on-screen (for example, 226) instructions to makethe necessary adjustments.

At the conclusion of an HRV measurement, numeric indicator 506 may showthe average heart rate in beats per minute, a measurement also derivedfrom demodulator 216 in FIG. 2, and a reading with which most users willbe familiar. This provides further confirmation of a good receivedsignal, and provides additional basic heart rate monitor functionality.Indicator 508 may show the calculated RMSSD value output from block 220,which is updated on display 226 every time a new valid RR Interval isprocessed. The raw RMSSD value may be further processed in order toderive a scale with benefits for end users and medical personnel. Forinstance, a logarithm, e.g., the natural logarithm, of the RMSSD valuemay be taken and multiplied by a scale factor, e.g., 20, to generate aHRV index; these operations may be realized in hardware and/or software,for example, by software running on CPU 310, special purpose digitalhardware, or a combination of software and hardware, using techniquesknown in the art. This results in a scale with approximate maximum 100for humans with very high heart rate variability, such as eliteathletes, and close to 0 for a heart rhythm with no short termvariability, such as may be found in a denervated or paced heart, or asubject in a mental state of coma. It also means that changes over aperiod of time result in constant numeric additive or subtractive offsetvalues independent of starting number. This can be very useful forcomparing the impact of stressful events such as athletic training &competition between users, and can also allow these changes to remain ofsimilar value as the user's steady state HRV levels change, e.g., due toincrease/decrease in fitness, or due to aging. The measurement iscompleted after a predefined number of breathing cycles, in oneembodiment 7 cycles of 8 seconds giving a measurement time of 56seconds.

Referring now to FIG. 6, the most recent HRV index measurement isdisplayed in the context of previous measurements by means of graphicline 602, in order that the user may easily visualize significant day today changes, as well as long term trends. The time axis 606 may belinear, with 4 different timescales, for example, 1 week, 1 month, 3months, all data. For example, two lines may be shown: daily pointsjoined by straight segments (line 602) and rolling 7 day average line603. Example thresholds may be based on absolute unit changes or may bestatistically derived from current and previous HRV index measurements.An initial 1 day significant decrease may be shown in amber, a furthersignificant decrease on subsequent days may be shown red. Alternatively,the time axis 606, may be displayed in logarithmic form in order to showdiscrete data points and emphasize changes over most recent days whilestill being able to visualize long term trends over months or evenyears. Numeric indicators 608, 610 and 612 may show differences betweenthe most recent reading and that of the previous day, week and monthrespectively. These indicators may optionally be colored to show trendswith example thresholds of 4 HRV index units in either direction. Forexample, in particular, an increase of 4 could be considered beneficialand be colored green, whereas a decrease of 4 could be consideredharmful, and shown in red. Changes within these limits might remain witha neutral color, or alternatively, may progressively shade green or red.The entire chart area between axes 604 and 606 may optionally also beshaded from red at HRV index 20 to green at HRV index 100, with theregion around HRV index 50-60 being neutral yellow.

Since exercise is an important part of maintaining cardiovascularhealth, it may be useful to track a training load quantity which isindependent of the type of sport performed, as a method of capturing theduration and perceived effort level of the user. In some embodiments,such a metric, for example, session RPE (Rating of Perceived Exertion)as proposed by Foster et. al. in Sports & Conditioning Research Journal2001; 15(1), 109-115 is tracked. For example, the RPE value may becaptured on a 1-10 scale and multiplied by the exercise duration inhours in order to obtain a Training Impulse (TRIMP) score. It isproposed to take this method of capturing the duration and perceivedeffort level of the user. For example, the user 202 may be instructed tochoose an exercise duration that best fits their session from a dropdown list displayed on the user interface 226, or they may enter a valueusing input device 224. This recording of periodic training load valuesby the user may be realized by hardware and/or software, for example, bydisplay device 226, input device 224 and software running on a CPU, suchas CPU 310, within device 228. The Perceived Exertion rating may bederived as per the reference using, e.g., a 10 point scale, againentered using microphone 318 or data entry mechanism 224. Alternatively,the TRIMP score may be entered directly from, or based on calculationsfrom a heart rate monitor device worn by the user during exercise. Themeasure of training load, for example, the Training Impulse score, maybe stored in, and subsequently retrieved from a database, for example,the same database 222 containing the periodic HRV readings, oralternatively, a second database, by hardware and/or software, forexample, memory control hardware and software known in the art.Subsequently, the periodic training loads may be displayed on, forexample, display 226, e.g., as a histogram as part of a chart displayingHRV readings, to allow an easy visual correlation between exercise leveland changes in HRV. An illustrative example could be a recreationalathlete who is training for an event, but who does a number of hardtraining sessions on consecutive days. It is likely that their HRV dailyreadings would decline, and they would be able to associate this withthe training load columns of the chart, make adjustments to theirtraining schedule, for instance to increase the amount of rest andrecovery, thereby leading to an optimization in daily HRV readings. Thehistogram may be computed in hardware and/or software, for example, bysoftware running on a digital CPU such as CPU 310, special purposedigital hardware, or a combination of software and hardware.

Referring now to FIG. 7, daily training load is recorded by the user atstep 702. This may, for example take the form of user 202 keying in avalue, for example, the RPE value, using control panel 224 of portabledevice 228. Alternatively, user 202 may enter the value into device 228verbally, in embodiments wherein device 228 supports speech recognition.Daily training load and HRV are stored in databases 706 and 710 (whichmay comprise portions of the same database, e.g., database 222 in someembodiments), at steps 704 and 708, respectively. Using counter 712, atstep 714 it is determined, for example by software running on CPU 310,whether enough daily values of training load have been stored indatabase 706 to be able to compute a valid histogram of training load,at the first instance of accumulating training load and HRV values,and/or subsequently, after a reset of counter 712 initiated by the userusing, for example, a user interface (for example control panel 224 ofFIG. 2 in some embodiments) of device 728 (for example, device 228 ofFIG. 2 in some embodiments). The value of counter 712 may for example bestored in a memory 312 by CPU 310. At steps 716 and 718, values oftraining load and HRV may be retrieved from databases 706 and 710,respectively, for example after a command to display a comparison chartof training load and HRV is entered by user 202 using control panel 224,in some embodiments. At step 720, a histogram of training load may becomputed, for example, by CPU 310 over a user-selected time period andpresented to the user 202 visually, for example as bar-type chart 722,on display 726 of device 728. A line-type chart 724 comprising HRVvalues for substantially the same time period as that for which thehistogram 722 is computed may also be presented at substantially thesame time on display 726 of device 728.

In some embodiments, recommendations to end users may be presented,based on the sequence of daily readings, and in particular theirrelative values over time. In general, recommendations presented by theinvention may, unless otherwise described herein, be presented using anycombination of hardware and software. In particular theserecommendations may be presented using, for example a display such asdisplay 226, and/or by audible output using, for example, a combinationof software running on CPU 310, D/A converter 318 implemented inhardware, and speaker 320 or earphones 322 connected to, for exampledevice 228. Although these recommendations for athletes may be presentedin numerous ways, they may be especially helpful when they are under 3simple headings:

1. REST—avoidance of training for a period of time

2. TRAIN—undertake structured training at different intensity and volumelevels

3. COMPETE—indicate that based on HRV readings, the user is currentlylikely to be capable of good performance levels in a forthcoming event

The theory relating to parasympathetic HRV that underpins thesedirections is as follows:

1. REST. A significant (as assessed by statistical tests, known in theart, on previous daily values) decline in daily reading from one day tothe next independent of all other indications indicates that the user'sbody is experiencing a significant physiological or psychological stressfrom which they need some period of time to recover.

2. TRAIN. A reading which is in line with, or above the recent trend(assessed by geometric mean or other averaging technique) of readings,and assuming that there are no contraindications from HRV or othersymptoms in the user.

3. COMPETE. The theory of periodization indicates that in order tomaximize performance in competition, a period of strenuous training(also known as functional overreaching) should be followed by a periodof lighter training or rest in order for supercompensation (when thebody becomes stronger) to occur. Sports coaches have discovered that theperiod of rest should be followed by some intense sessions to preparethe body for the rigors of competition.

In HRV sequence terms, this means that a depressed mean value duringtraining may be followed by a significant rise in the mean value (likelyto a level higher than seen in previous months) during recovery,followed by a dip before competition.

For example, the present invention, may have indicators for 3 timeperiods, respectively: a) Daily Change, b) Weekly Change, c) MonthlyChange

All 3 indicators may assume any of the following color values withassociated significance:

1. Blue (B)—no significant change

2. Amber (A)—significant negative change

3. Red (R)—highly significant negative change

4. Green (G)—significant positive change

Putting together the above desired recommendations and indicators, maygive the following table of interpretations:

Daily Weekly Monthly Change Change Change Rest Rest for one day R G/B/AG/B/A Rest for 2-3 days A A/R B/A Rest several days R A A Extended restR R R Train Light training A G/B G/B Normal training B G/B G/B Intensetraining G G/B G/B Compete Ready to compete G/B B/A B/A

Recommendations may be presented to the user in the form of a table inthe printed instructions to the user, as shown above, or, alternatively,by inputting the Daily/Weekly/Monthly Change indications into expressionlogic, for example, within the code executed by processor 310, whichthen outputs the relevant recommendation either via text on, forexample, display 314, or via a speech synthesized instruction outputfrom speaker 320 or earphones 322.

Referring now to FIG. 8, Daily, Weekly, and Monthly HRV change may becomputed, for example, at steps 802-806, and significance may beassigned to these changes at steps 808-812, respectively, for example,as previously described. HRV change and significance value assignmentmay be realized in hardware and/or software, for example, by softwarerunning on CPU 310, special purpose digital hardware, or a combinationof software and hardware. Color-coding may be assigned to thesignificance values assigned to daily change, weekly change, and monthlychange at steps 814-818, respectively. Training recommendations may bepresented to the user, for example, in the form of color-codedindicators 830-834 of the significance of daily, weekly, and monthlychanges in HRV labeled as such by texts 824-828, respectively on display820 of device 822. For example, using the previously described colorcode assignments, the daily change may be assigned the value “highlysignificant negative change” and the indicator 828 may be assigned thecolor red. The weekly and monthly changes may both be assigned the value“significant negative change” and the color amber, and these colors maybe displayed by indicators 832 and 834, respectively, on display 820 ofdevice 822. Based upon the values assigned to the daily, weekly andmonthly changes, training recommendations may be presented to the user,for example, as text 836 on a portion of display 820 of device 822.Indicators 830-834 and recommendations 836 may be presented by hardwareand/or software, for example, by software running on CPU 310, anddisplay control hardware and a display 820 known in the art.

In some embodiments, the user may input a periodic, for example, dailymood score, using for example, the control panel 224 of device 228. Themood score (which may be based on fatigue, stress or other mood statesknown in the art) may, for example, comprise a number from 1 to 5, with1 representing “happiest” and 5 representing “unhappiest”. The systemmay store the daily mood score in a first database (e.g., database 222in some embodiments). The system may retrieve a set of HRV values from asecond database, e.g., database 710 of FIG. 7, which may comprise aportion of the same database used to store the mood scores, e.g.,database 222 of FIG. 2. Referring now to FIG. 9, the device, forexample, device 228 may display, for example, day names 910 and/or dates912, mood scores 916, and HRV values 914 simultaneously, for example ondisplay 226, so that the user can correlate her mood with her HRV value.In some embodiments, icons 918 representing mood scores may be displayedinstead of or in addition to numerical mood scores 916. For example, anicon 920 representing a “very smiley face” may be displayed, eitheralone or in close proximity to the number 1, to represent the “happiestmood”, a “somewhat smiley face” icon 922 may be displayed, either aloneor in combination with the number 2, to represent a somewhat happy mood,a “neutral face” icon 924 (horizontal mouth line) may be displayed,either alone or in combination with the number 3, to represent a neutral(neither happy nor unhappy) mood, a “somewhat frowning face” icon 926may be displayed, either alone or in combination with the number 4, torepresent a “somewhat unhappy” mood, and a “very frowning face” icon 928may be displayed, either alone or in combination with the number 5, torepresent a “very unhappy” mood. The purpose of the mood recording,display, and correlation may be for example to assist the user indiagnosing the onset of overtraining, where the HRV value alone may notprovide conclusive enough evidence. It may also assist in the detectionof training monotony, independent of HRV value, where too much of thesame type of training is being performed.

As opposed to the usual prospective study group statistical approach,The method and system described above introduces the capability toperform self contained measurements, permitting long term longitudinalstudy of HRV variations within individuals, by taking periodic, forexample, daily, measurements for only one individual over the long term.

In some embodiments, a measure of fitness is computed from a set of HRVvalues compiled over an extended period of time. For example, HRV valuescompiled on a daily basis may be stored, for example, in database 222 ofFIG. 2, for weeks, months, or even years. A first fitness measure maythen be computed using, for example, HRV values compiled over a firstone-week period. This first fitness measure may then be presented to theuser, together with a second fitness measure computed using stored HRVvalues for a second, later one-week period for example, on display 226of device 228. The user may then be able to appreciate her relativechange in fitness to assess, for example, the success or lack thereof ofher training program. The user may also be able to take preventativemeasures by, for example, consulting her physician, should thedifference in presented fitness measures indicate a marked deteriorationin fitness.

In another example, the user may be presented with a measure of herfitness on a short term, e.g., daily basis, even though that measure offitness is computed for an extended period of time, for example theone-month period preceding the current day.

A measure of a user's relative fitness for an extended time period maybe computed by taking the difference between the current average HRVvalue and the average value stored at an earlier point in time, usuallyseveral weeks or months previously, and under comparable conditionse.g., following a rest period of several days, e.g., in database 222,which may, for example, comprise a portion of memory 312. Measures ofrelative fitness may be computed, for example, by software running on aCPU 310, special purpose digital hardware, or a combination of softwareand hardware. If the user's fitness at the earlier time point is known(for instance expressed as VO2 peak), then an increase in average HRV islikely to signify an improvement in VO2 peak, and vice versa for anegative change.

In another embodiment, the method and system may provide thefunctionality of an HRV-based alarm clock. For example, device 110,which may comprise device 228 in some embodiments, may generate anawakening stimulus to user 102 (user 202 in some embodiments) designedto awaken the user when the difference between the HRV index measuredwhile the user is sleeping and the HRV index measured prior to the userfalling asleep exceeds a predetermined threshold. In general, generatingan awakening stimulus may be realized by a combination of softwareand/or hardware, for example, generating the stimulus may be controlledby software running on CPU 310 and the stimulus itself may be deliveredto the user by means of hardware known in the art. For example, sincethe increase in the parasympathetic HRV index, that the presentinvention measures, back towards the rolling average is associated withrecovery, the device 110 may be run continuously while the user isasleep in one specific embodiment and an awakening stimulus, forexample, a loud sound, may be generated by the device 110 to wake theuser up when sufficient recovery has occurred, as indicated by thedifference between waking and sleeping HRV index exceeding apredetermined threshold. In calculating the difference between wakingand sleeping HRV, the waking HRV value may be stored in, for example,memory 312 or any other suitable storage device. The generation of theloud sound may, for example be controlled by software running on CPU 310and the generation of the sound itself may be via D/A converter 318 andspeaker 320. Alternatively, device 110 may provide a direct stimulus tothe user via a wired or wireless link to a transducer (not shown)attached to the user 102. For example, a vibrating transducer around theuser's wrist may receive a vibrate command wirelessly from device 110,for example, under the control of software running on CPU 310, andtransmitted by modulator 324. This HRV-based alarm clock function may beof special value to people who need to stay awake as much as possible,but still need to function well, e.g., round the world sailors,expedition members, military on assignment, top politicians, businessleaders etc., but may also be of value to any user who needs to maximizeher productivity while also achieving sufficient recovery after sleep.

In another embodiment, a periodic HRV measurement may be used formanagement of hypertension and pre-hypertension in individuals. It isknown that low and reducing values of parasympathetic HRV can precedeeither the development, or worsening, of hypertension. It is also knownthat frequently recommended lifestyle modifications, such as increasinglevels of exercise, dietary fruit and vegetables, rest, sleep andrelaxation, and reducing body weight, stress, smoking, alcohol anddietary salt all have a significant and often rapid impact onparasympathetic activity which can be reflected in a periodic HRVmeasurement.

Referring now to FIG. 10, which may, for example, be partly implementedby CPU 310 executing instructions fetched from a portion of memory 312,at step 1002, in this embodiment, a user may input answers, for exampleusing control panel 224 under the control of software, for examplerunning on CPU 310, in response to a series of personal profilequestions, which may be presented to the user for example, on display226, under the control of software, for example running on CPU 310. Asone example, these questions may include:

1. Age

2. Sex

3. Body weight

4. Body Mass Index (or hip-waist size ratio)

5. Amount of exercise (hrs per week, or TRIMP)

6. Smoking (cigarettes per day)

7. Alcohol intake (units per week)

8. Sleep amount (hrs per night)

9. Overall stress score

10. Overall mood score

11. Family history of heart disease (positive value if either naturalparent has a history of cardiovascular disease)

12. Blood pressure (last known reading)

13. Nutrition (number of fruit and vegetable servings per day)

The answers to these questions may be utilized is as follows:

At step 1004 an initial HRV reading for the user may be capturedaccording to the method described in the preceding paragraphs, At step1006, a Heart Health Index may be generated by comparing the value ofthe initial HRV reading against, for example, pre-stored values of normsof HRV in both sexes of the general population within a particular agerange that includes the current age of the user. This comparison may beperformed by any combination of hardware and software, for example, bysoftware running on CPU 310 and/or special purpose hardware, usingtechniques known in the art. These HRV norms may be based on datapreviously published in scientific papers, or may be established bypooling data from many users of the present invention. The HRV norms maybe pre-stored, for example, in a portion of memory 312, by downloadingunder the control of CPU 310, using a wired or wireless interface (notshown in FIG. 3).

At step 1008, an initial summary may be presented to the user, forexample, on display 226, indicating the percentile range within whichtheir age and sex normalized HRV reading sits.

At step 1010, for each of the parameters 3-13 in the above list, a scoremay be generated on, for example, a 5 point scale (from +2 to −2)indicating the better/worse deviation of the answers fromrecommendations provided by, e.g., the US National Institute of HealthJoint National Committee in the 7th report of prevention, detection,evaluation and treatment of high blood pressure, or other publicadvisory body on cardiovascular health. These scores may be generated byany combination of hardware and software, for example, by softwarerunning on CPU 310 and/or special purpose hardware, using techniquesknown in the art.

At step 1012, a set of recommendations for improvement of the HRV valuemay be generated and presented to the user, for example on display 226,based on the scores obtained from step 1010. For example, if the user'sBMI is greater than 30 then they will be urged to lose weight, and iftheir exercise score is also low, to include an exercise program whoseintensity and frequency may also be prescribed using the method, systemand software described in the preceding paragraphs.

The following table shows one example of a recommendation of lifestylemodifications in order to improve HRV reading (and consequentlycardiovascular health) that may be presented to the user.

Initial Compared to HRV compared Recommendation Parameter Value norm(points) to norm to user Body Mass 30 −2 −1 walk 90 minutes Index perweek

Overtraining is a potentially serious condition which occasionallyaffects athletes who are involved in periods of very high intensitytraining, and typically takes a period ranging between a few days up toseveral months of complete rest in order to recover.

The medical diagnosis of overtraining is currently performed usingsymptoms of persistent tiredness, unusually high (or sometimes low)resting heart rate, low mood score, and an inability to perform atnormal levels during training or competition. In addition, there hasbeen scientific interest in the use of heart rate variability toincrease the robustness of diagnosis, but no definitive methodincorporating this variable has yet been proposed.

This diagnosis & cure of overtraining has three main problems in thesituation when an athlete presents themselves to a medical professional:

1. It is difficult to distinguish between the above mentioned symptomsof overtraining and those of other illnesses, particularly sinceovertraining itself often weakens the immune system, allowing thesymptoms of secondary infections to manifest themselves.

2. The symptoms of overtraining can be confusing, for instance in oneform of overtraining, known as “parasympathetic overtraining”, theathlete can present with a low resting heart rate, normally indicativeof a well recovered state, yet they are unable to perform at theathletic level expected of them.

3. By the time the athlete presents themself to a medical professional,the overtraining state may be well established, meaning that the athletewill need to be out of action for a substantial period of time whilethey recover.

A method of detecting overtraining is described hereinafter, based onthe following principles:

1. The Polyvagal Theory (1995-2007) of Professor Stephen Porges. Ref:Biological Psychology 74 (2007) 116-143.

2. The time progression of overtraining cardiac markers, particularlyresting heart rate (RHR) and parasympathetically mediated respiratorysinus arrhythmia (RSA) HRV, as measured and quantified using the naturallogarithm of the RMSSD.

Taking these in turn:

1. The Polyvagal Theory, conceived and usually applied in the field ofpsychophysiology, proposes that the parasympathetic nervous system ofmammals has two distinct branches, originating in different parts of thebrain, and carried by different types of nerves to the pacemaking(sinoatrial) region of the heart.

The Polyvagal Theory argues that when an animal's central nervous systemis presented with a challenge (which can be either from the animal'senvironment, or from its own internal organs, or viscera), the autonomicnervous system (in total) of that animal will attempt to deal with thechallenge by successively activating branches of the ANS in thefollowing order:

i) Parasympathetic nervous system, with stimulus originating in theNucleus Ambiguous of the brainstem, carried via myelinated nerves to theheart, and having the impact of reducing heart rate rapidly (1 sec orless) during expiration.

ii) Sympathetic nervous system, carried by unmyelinated nerves, andhaving the impact of increasing heart rate over a period of severalseconds.

iii) Parasympathetic nervous system with stimulus originating in thedorsal motor nucleus, carried via unmyelinated nerves, and having theimpact of reducing heart rate over a period of several seconds.

2. The use of a daily (or other frequent periodic) measurement of twovariables—resting heart rate (RHR) and a measure of HRV, for example, LnRMSSD, can be used to separate out the specific states in the aboveprogression according to the following table, where “Normal” refers to arolling average of recent values (for instance the past 7 days), and“Reduced” or “Increased” refer to the numerical relationship between thecurrent value and the “Normal” value, having passed tests of statisticalsignificance.

STATE TIME Ln RMSSD RHR Recovered 0 Normal Normal After training 1Reduced Normal “Sympathetic” 2 Reduced Increased overtrained“Parasympathetic” 3 Reduced (in a few Decreased overtrained cases may benormal or even increased)

In one embodiment, athletic overtraining is measured and ameliorated asdescribed below with reference to FIG. 11. At step 1102, the heart rateof a resting user (Resting Heart Rate or RHR) may be measured beforetraining. This measurement may be performed by any combination ofhardware and software, for example, by software running on CPU 310and/or special purpose counter/timer hardware, such as described in thepreceding paragraphs with reference to function 218. At step 1104, HRVmay also be measured, for example as Ln RMSSD, computed, for example asthe output of block 220. At step 1106, it may be determined, e.g., bysoftware running on CPU 310, whether enough RHR and HRV measurementshave been taken to insure that reliable “normal” values, for exampleaverage values, of RHR and HRV may be computed by any combination ofhardware and software, for example, by software running on CPU 310and/or special purpose hardware, using techniques known in the art.

If sufficiently reliable values of RHR and HRV are available at step1108, then RHR may be measured after training at step 1110. At step1122, it may be determined whether the value of RHR after training islower than the value of RHR before training by a predetermined amount.If so, then at step 1124, recommendations relevant to parasympatheticovertraining, as described hereinbefore, for example, recommendations onreducing training load or recommendations on resting, may be presentedto the user. If, on the other hand, the value of RHR after training isnot lower than the value of RHR before training, then at step 1112 thevalue of HRV after training may be measured. At step 1114 it may bedetermined whether the value of HRV after training is lower than thevalue of HRV before training by a predetermined amount. If not,recommendations relevant to normal training, as described hereinbefore,for example, with reference to FIG. 8, may be presented to the user atstep 1116. If, on the other hand, HRV is reduced, then at step 1118 itmay be determined if the value of RHR after training is higher than thevalue of RHR before training. If so, then at step 1120, recommendationsrelevant to sympathetic overtraining, for example, recommendations onreducing training load or recommendations on resting, may be presentedto the user. If, on the other hand, the value of RHR after training isnot higher than the value of RHR before training, then recommendationsrelevant to normal training may be presented to the user at step 1116.

Thus, it may be possible to provide an unambiguous diagnosis ofdifferent states of overtraining, and furthermore, by identifying theprogression, avoid the development of the most dangerous form known asParasympathetic Overtraining by reducing training load and building inrest and recovery at a much earlier stage.

In another embodiment, the exercise dose for patients with heart failuremay be prescribed. Regular aerobic exercise in heart failure patientshas been established as a beneficial lifestyle modification that couldsignificantly improve their prognosis and decrease costs associated withchronic care. A conundrum exists between the need to carry out exerciseat sufficient intensity and duration to bring about beneficial changeswhile taking care not to overload patients whose tolerance forphysiological stress may be very low. Vagal efferent activity has beenshown to be strongly negatively correlated with short term overload andpositively with beneficial longer term cardioprotective adaptations. Itcan be measured non-invasively by examining respiration mediatedvariation in beat to beat intervals (RSA).

The target population of heart failure (HF) patients present additionalchallenges compared to healthy subjects for several reasons:

1. The RR intervals of HF patients are more likely to possess sinusarrythmia of non-respiratory origin than healthy subjects. Obtainingsufficient consistency in daily measurement will necessitate moreadvanced forms of automatic beat interval screening, removal and/orcorrection than are currently used in a healthy population. Knowledge ofthe current phase in the paced breathing cycle can be used to determinethe likelihood of a measured beat interval value being correct.

During inhalation, as identified at step 406, the autonomic nervoussystem (parasympathetic branch) of the user withdraws stimulation, withthe consequence that the R-R interval is shortened. Since the method,for example as previously described with reference to FIGS. 1 and 2,includes a controlled breathing pattern, beats that are longer than theaverage R-R interval (which may be calculated over one or more completebreath cycles) during inhalation may be identified at step 410 andexcluded at step 414. In practice an additional margin, shown as x instep 410, may be included to allow for the fact that the user'sbreathing may not be perfectly synchronized with, e.g., the lunganimation 106 of FIG. 1.

During exhalation, as identified as not-inhalation at step 406, amaximum allowed difference between adjacent R-R intervals can also bepredicted by knowing the average R-R interval taken over one or morecomplete breath cycles during the measurement. This is possible becausethe parasympathetic-sympathetic balance also controls the standing pulserate. A formula has been determined empirically from observations onmultiple users, whereby the maximum permitted R-R interval differencemay be related to the square of the mean R-R interval divided by aconstant, for example 12000. Therefore, R-R intervals not conforming to:

Max allowed adjacent R-R interval difference=(mean R-R interval)²/12000

may be identified at step 412 and excluded at step 414. As previouslydescribed, the mean R-R interval calculation may commence earlier thanthe HRV calculation during the measurement sequence so that all R-Rintervals considered for inclusion in the HRV calculation may beadequately assessed for validity. Intervals not identified for exclusionaccording to the preceding criteria are included in the HRVcalculations, e.g., the RMSSD calculations of processor 220, at step416.

2. Heart failure patients frequently suffer from atrial fibrillation(AF), and while it has been shown in the literature that vagalmodulation is still apparent in the RR interval, the measurement basismay be significantly different to that of the same patient not sufferingfrom AF. Therefore, intra-patient comparisons will have to be performedin order to compare HRV values with and without AF and establishnormative/conversion values in the device.

In the case that a user has a temporarily irregular heart rhythm, suchas palpitations, Atrial Fibrillation, or SupraVentricular Tachycardia,this may lead to a number of excluded intervals according to the set ofrules outlined in the previous paragraphs. In case the number of suchexcluded beats exceeds a predefined threshold, e.g., 5, during themeasurement sequence, then the measurement will be terminated and theuser informed of the reason, and instructed to retake the measurementafter some period of time. This is in order to prevent incorrectreadings from contaminating the calculation of longitudinal averages andother statistical measures upon which color codes and recommendationsare based.

3. Cardiac patients have significantly decreased HRV compared to healthycontrols, and vagally modulated HRV is lower even for healthy controlsin the standing position. Patients with advanced HF may also havetrouble standing, so it may be that either sitting or supine positionsare best for performing the daily test. This is not likely to result insaturation of parasympathetic stimulus, as it might do for an athleticindividual.

4. A paced breathing pattern of long duration (example 8 seconds)suitable for healthy subjects may not be applicable to patients withdyspnea (shortage of breath), therefore it may be that a paced breathingpattern with shorter periods of controlled inhalation, expiration andholding times needs to be used for HF patients. It is preferable to havethe expiration as the longest phase of the breathing pattern, so forexample an inhalation period of 2 seconds, followed by expiration of 3seconds may be used.

5. Heart failure patients will have a lower tolerance for homeostasisdisturbance resulting from exercise, therefore suitable thresholds fordaily exercise recommendation may need to be based on multiplealgorithmic rules reflecting maximum allowable inter day changes invagal HRV. Recovery times will also be longer than in healthy subjects,and these also need to be taken into account. In some embodiments anindividualized exercise prescription may be conveyed to the user eithervia text on the screen of the device, or perhaps voice synthesis. Thisneeds to be demanding enough to stimulate supercompensation withassociated improvement in autonomic function, but not so demanding thatit causes increased risk to the patient.

6. Heart failure patients will have lower tolerance to exertion duringexercise than healthy subjects. It is known that parasympatheticstimulus is withdrawn as exercise intensity increases, and also knownthat parasympathetic activity is cardioprotective against potentiallylethal arrhythmia, to which heart failure patients may be susceptible.For this reason it is desirable that parasympathetic activity not fallbelow a safe level. This embodiment can therefore also be used in acontinuous measuring mode, whereby both the HRV and heart rate (HR) arecalculated, for example, every 10 seconds. As the user increases theirexercise intensity from rest (exercise types may include walking,cycling, jogging, rowing machine etc), their heart rate will increase inorder to pump more blood to their working muscles, and their HRV willdecrease, as parasympathetic stimulus is withdrawn. If the values ofboth parameters from successive measurements are compared, aparasympathetic withdrawal index (PWI) may be calculated as follows:

PWI=(HRV1−HRV2)/(HR2−HR1)

When PWI is less than a threshold value, a sonic and/or visual alarm maybe given to the user, urging them to reduce exercise intensity until thePWI once again exceeds the safe threshold.

In one embodiment, HRV for a user with heart failure is measured andexercise is recommended to improve the user's HRV measure as describedbelow with reference to FIG. 12. At step 1201, it may be determined thatthe user wants recommendations relevant to heart failure patients by,for example, presenting an option to select this response to the user ondisplay 226, under the control of software running on CPU 310, and/orusing audible output via speaker 320 or earphones 322. For the reasondescribed in the preceding paragraphs, at step 1202, the user may beinstructed to sit or lie down, for example, via a message on display226, under the control of software running on CPU 310, and/or usingaudible output via speaker 320 or earphones 322. At step 1204, HRVmeasurement may be started using the aforementioned specially-pacedbreathing pattern, for example, an inhalation period of 2 seconds,followed by expiration period of 3 seconds may be used. Irregular beatsmay be excluded using the method described in the preceding paragraphsat step 1206. At step 1208, excluded irregular beats may be compared toa predetermined threshold, and if this threshold is exceeded, at step1210 the measurement may be terminated, for example under the control ofsoftware running on CPU 310, and the user informed, for example, thatthe measurement could not be reliably taken and must be retaken sometime later. If the irregular beat threshold is not exceeded, then atstep 1212 the user may be presented with an exercise recommendationappropriate to a user with heart failure.

7. Centralized data collection should be performed more frequently thanfor healthy controls due to the increased risks that cardiac patientscarry. In this embodiment, the device 110 may have the ability tocommunicate, for example, using standard protocols such as WiFi, in thecase where device 110 is a PC, or GSM, in the case where device 110 is acell phone, the HRV reading and other databases to a central server (notshown) capable of storing and analyzing the results, and alerting amedical expert when needed, for instance if a declining trend of HRV isseen over an example period of 3 days or more.

Referring now to FIG. 13, in another embodiment, HRV for users withheart failure is measured continuously, as described in the precedingparagraphs. Prior to starting the flow of FIG. 13, it may be decidedwhether an HRV measurement should be started or re-started, based on,for example, input from the user to start or stop the continuousmeasurement mode of operation of, for example, device 228 and whether atimer, for example, a 10 second timer (not shown), has expired. If theuser has decided to start a new measurement, or in the case ofcontinuous measurements, the user has not stopped the continuousmeasurements, then at the start of each measurement interval, forexample, every 10 seconds, a new value of heart rate may be measured atstep 1306. Irregular beats may be excluded, as described in thepreceding paragraphs, at step 1308, and a new value of heart ratevariability may be calculated at step 1310. At step 1312, a new value ofPWI may be calculated, as described in the preceding paragraphs, byhardware and/or software, for example, by software running on a digitalCPU, special purpose hardware, or a combination of software andhardware. If PWI is less than a predetermined threshold, then at step1314, an alarm may be generated to the user. If PWI is not less than thepredetermined threshold, then no alarm is generated to the user. Thealarm may be generated by a combination of hardware and software, forexample, the alarm may be initiated by software running on CPU 310 andan alarm sound generated by D/A converter 318 and speaker 320.Alternatively, device 110 may be caused to vibrate by a transducer (notshown in the figures) controlled by software running on CPU 310.Alternatively to or in conjunction with device 110 generating an alarmsound or vibrating, a prominent message, for example, a flashing alarmmessage, may be generated on display 108 under the control of softwarerunning on CPU 310.

While the foregoing written description of the invention enables one ofordinary skill to make and use what is considered presently to be thebest mode thereof, those of ordinary skill will understand andappreciate the existence of variations, combinations, and equivalents ofthe specific embodiment, method, and examples herein. The inventionshould therefore not be limited by the above described embodiment,method, and examples, but by all embodiments and methods within thescope and spirit of the invention as claimed.

The embodiments of the present invention may be implemented with anycombination of hardware and software. If implemented as acomputer-implemented apparatus, the present invention is implementedusing means for performing all of the steps and functions describedabove.

The embodiments of the present disclosure can be included in an articleof manufacture (e.g., one or more computer program products) having, forinstance, computer useable or computer readable media. The media hasembodied therein, for instance, computer readable program code means,including computer-executable instructions, for providing andfacilitating the mechanisms of the embodiments of the presentdisclosure. The article of manufacture can be included as part of acomputer system or sold separately.

While specific embodiments have been described in detail in theforegoing detailed description and illustrated in the accompanyingdrawings, it will be appreciated by those skilled in the art thatvarious modifications and alternatives to those details could bedeveloped in light of the overall teachings of the disclosure and thebroad inventive concepts thereof. It is understood, therefore, that thescope of the present invention is not limited to the particular examplesand implementations disclosed herein, but is intended to covermodifications within the spirit and scope thereof as defined by theappended claims and any and all equivalents thereof.

1. A method of measuring heart rate variability of a user, comprising:instructing the user when to breathe in and breathe out; receiving asignal from a sensor responsive to the user's heart beat while the userbreathes in and out as instructed; processing the received signal todetermine heart beat intervals of the user; calculating a measure ofheart rate variability of the user from the processed heart beatintervals; and excluding irregular heart beat intervals from thecalculation of heart rate variability.
 2. The method of claim 1, furthercomprising: generating a first sound that indicates to the user when tobreathe in; and generating a second sound that indicates to the userwhen to breathe out.
 3. The method of claim 1, further comprisinggenerating synthesized audible output to the user selected from thegroup comprising instructions on how to place the sensor, instructionson when to breathe in, and instructions on when to breathe out.
 4. Themethod of claim 1, further comprising: inputting by the user on aperiodic basis a value of training load; storing the periodic trainingload values in a first database; storing periodic heart rate variabilitymeasures in a second database; retrieving from the first database a setof periodic training load values input over a predetermined period oftime; computing a histogram of the retrieved periodic training loadvalues; retrieving from the second database a set of periodic heart ratevariability measures corresponding to a period of time substantiallyequal to the predetermined period of time; and presenting the histogramand the set of heart rate variability measures to the user substantiallysimultaneously.
 5. The method of claim 1, further comprising: computinga first measure of short term change of the heart rate variabilitymeasure; computing a second measure of medium term change of the heartrate variability measure; computing a third measure of long term changeof the heart rate variability measure; assigning to the first changemeasure a first value of significance; assigning to the second changemeasure a second value of significance; assigning to the third changemeasure a third value of significance; and presenting to the user atsubstantially the same time indicators of the first value ofsignificance and the second value of significance and the third value ofsignificance.
 6. The method of claim 5, further comprising presentingrecommendations to the user based upon the first value of significanceand the second value of significance and the third value ofsignificance.
 7. The method of claim 1, further comprising: inputting bythe user on a periodic basis a value of mood; storing the periodic moodvalues in a first database; storing periodic heart rate variabilityvalues in a second database; retrieving from the first database a set ofperiodic mood values recorded over a predetermined period of time;retrieving from the second database a set of periodic heart ratevariability values corresponding to a period of time substantially equalto the predetermined period of time; and presenting the set of moodvalues and the set of heart rate variability values to the usersubstantially simultaneously.
 8. The method of claim 7, wherein thepresented mood values are displayed as icons.
 9. The method of claim 1,further comprising: storing a number of successively measured heart ratevariability measures in a database; retrieving from the database a firstset of heart rate variability measures stored over a first predeterminedextended period of time; retrieving from the database a second set ofheart rate variability measures stored over a second predeterminedextended period of time subsequent to the first extended period of time;and computing a measure of fitness of the user based on the first set ofretrieved heart rate variability measures and the second set ofretrieved heart rate variability measures.
 10. The method of claim 9,further comprising presenting successive instances of the measure offitness to the user on a regular basis.
 11. The method of claim 9,further comprising: presenting to the user a first measure of fitnesscomputed for a first period of time; and presenting to the user, atsubstantially the same time, a second measure of fitness computed for asecond period of time substantially different from the first period oftime.
 12. The method of claim 1, further comprising: presenting to theuser a set of personal profile questions including at least age and sex;inputting from the user answers to the questions; capturing an initialheart rate variability measure; comparing the initial heart ratevariability measure to heart rate variability norms appropriate to theage and sex of the user; presenting to the user the result of thecomparison; generating scores based on the deviation of the answers fromcorresponding values recommended by at least one advisory body oncardiovascular health; and presenting based on the scores at least onerecommendation for improving the heart rate variability measure of theuser.
 13. The method of claim 1, further comprising: measuring restingheart rate and heart rate variability of the user a number of timessufficient to compute a valid normal value of resting heart rate and avalid normal value of heart rate variability before the user engages inphysical training; computing the valid normal value of resting heartrate and the valid normal value of heart rate variability of the user;measuring resting heart rate after the user engages in physicaltraining; presenting at least one recommendation to the user relevant toparasympathetic overtraining if the resting heart rate of the usermeasured after training is less than the normal resting heart rate ofthe user; measuring heart rate variability after the user engages inphysical training if the resting heart rate of the user after physicaltraining is not less than the normal resting heart rate of the user;presenting at least one recommendation to the user relevant tosympathetic overtraining if the heart rate variability of the usermeasured after training is less than the normal resting heart ratevariability of the user and the resting heart rate of the user measuredafter training is greater than the normal resting heart rate of theuser; presenting at least one recommendation to the user relevant tonormal training if the heart rate variability of the user measured aftertraining is not less than the normal resting heart rate variability ofthe user; and presenting at least one recommendation to the userrelevant to normal training if the heart rate variability of the usermeasured after training is less than the normal heart rate variabilityof the user and the resting heart rate of the user measured aftertraining is not greater than the normal resting heart rate of the user.14. The method of claim 1, further comprising: determining if the userdesires recommendations relevant to heart failure patients; instructingthe user to sit or lie down; wherein instructing the user when tobreathe in and breathe out comprises instructing the user to breathe inmore quickly and breathe out more quickly than would be the instructionto a user who does not suffer from heart failure; computing the numberof excluded beats over a predetermined period of time; terminating theheart rate variability measurement if the number of excluded beatsexceeds a predetermined threshold; and presenting at least one exerciserecommendation appropriate to the user based on the measure of heartrate variability if the number of excluded beats does not exceed thepredetermined threshold.
 15. The method of claim 1, further comprising:calculating heart rate from the processed heart beat intervals afterexcluding irregular heart beat intervals; calculating a ParasympatheticWithdrawal Index using the current value of the heart rate and a priorvalue of the heart rate and the current value of the heart ratevariability measure and a prior value of the heart rate variabilitymeasure; and generating an alarm to the user if the ParasympatheticWithdrawal Index is less than a predetermined threshold.
 16. The methodof claim 1, wherein calculating the measure of heart rate variabilityfrom the processed heart beat intervals uses a time domain processingmethod and wherein the time domain processing method is selected fromthe group comprising RMSSD, pNN50, and SD1 cluster Poincare plot. 17.The method of claim 1, further comprising: calculating an averagecomprising the current measure of heart rate variability and previousmeasures of heart rate variability; and presenting to the user theaverage.
 18. The method of claim 17, wherein the average comprises arolling average.
 19. The method of claim 18, wherein the rolling averagecomprises a seven day average.
 20. The method of claim 17, wherein themeasure of heart rate variability comprises the natural logarithm ofRMSSD.
 21. The method of claim 1, further comprising presenting to theuser one or more recommendations on type or intensity of training toperform.
 22. The method of claim 21, wherein the recommendations arepresented as text on a device operated by the user.
 23. A method ofmeasuring heart rate variability of a user, comprising: receiving asignal from a sensor responsive to the user's heart beat; processing thereceived signal to determine heart beat intervals of the user;calculating a measure of heart rate variability of the user from theprocessed heart beat intervals; excluding irregular heart beat intervalsfrom the calculation of heart rate variability; calculating the numberof excluded heart beats over a predetermined period of time; andpresenting to the user the number of excluded heart beats.
 24. Themethod of claim 23, wherein the predetermined period of time comprisesone hour.
 25. The method of claim 23, further comprising: maintaining arunning count of the number of consecutive heart beats that are excludedwithin the predetermined period of time; and presenting to the user anindication if the running count is greater than or equal to three.
 26. Asystem for measuring heart rate variability of a user comprising: meansfor instructing the user when to breathe in and breathe out; means forreceiving a signal from a sensor responsive to the user's heartbeatwhile the user breathes in and out as instructed; means for processingthe received signal to determine heart beat intervals of the user; meansfor calculating a measure of heart rate variability of the user from theprocessed heart beat intervals; and means for excluding irregular heartbeat intervals from the calculation of heart rate variability.
 27. Acomputer software product embodied in a non-transitory computer-readablephysical medium comprising coded instructions for executing a computerprocess in a digital processor, which computer process generates ameasure of heart rate variability, the computer process comprising:instructing a user when to breathe in and breathe out; inputtingprocessed heart beat intervals; wherein the processed heart beatintervals are output by a signal processing means and an input of thesignal processing means comprises a signal received by a receiving meansfrom a sensor responsive to the user's heartbeat while the user breathesin and out as instructed; calculating a measure of heart ratevariability of the user from the processed heart beat intervals; andexcluding irregular heart beat intervals from the calculation of heartrate variability.
 28. The computer process of claim 27, furthercomprising executing at least a portion of the signal processing means.29. The computer process of claim 27, further comprising executing atleast a portion of the receiving means.
 30. The computer process ofclaim 27, further comprising: managing generation of a first sound thatindicates to the user when to breathe in; and managing generation of asecond sound that indicates to the user when to breathe out.
 31. Thecomputer process of claim 27, further comprising managing generation ofsynthesized audible output to the user selected from the groupcomprising instructions on how to place the sensor, instructions on whento breathe in, and instructions on when to breathe out.
 32. The computerprocess of claim 27, further comprising: calculating a measure of heartrate variability of the user from the processed heart beat intervals;managing inputting by the user on a periodic basis a value of trainingload; storing the periodic training load values in a first database;storing periodic heart rate variability measures in a second database;retrieving from the first database a set of periodic training loadvalues input over a predetermined period of time; computing a histogramof the retrieved periodic training load values; retrieving from thesecond database a set of periodic heart rate variability measurescorresponding to a period of time substantially equal to thepredetermined period of time; and managing presenting the histogram andthe set of heart rate variability measures to the user substantiallysimultaneously.
 33. The computer process of claim 27, furthercomprising: computing a first measure of short term change of the heartrate variability measure; computing a second measure of medium termchange of the heart rate variability measure; computing a third measureof long term change of the heart rate variability measure; assigning tothe first change measure a first value of significance; assigning to thesecond change measure a second value of significance; assigning to thethird change measure a third value of significance; and managingpresenting to the user at substantially the same time indicators of thefirst value of significance and the second value of significance and thethird value of significance.
 34. The computer process of claim 33,further comprising managing presenting recommendations to the user basedupon the first value of significance and the second value ofsignificance and the third value of significance.
 35. The computerprocess of claim 27, further comprising: managing inputting by the useron a periodic basis a value of mood; storing the periodic mood values ina first database; storing periodic heart rate variability values in asecond database; retrieving from the first database a set of periodicmood values recorded over a predetermined period of time; retrievingfrom the second database a set of periodic heart rate variability valuescorresponding to a period of time substantially equal to thepredetermined period of time; and managing presenting the set of moodvalues and the set of heart rate variability values to the usersubstantially simultaneously.
 36. The computer process of claim 35,wherein the presented mood values are displayed as icons.
 37. Thecomputer process according to claim 27, further comprising: storing anumber of successively measured heart rate variability measures in adatabase; retrieving from the database a first set of heart ratevariability measures stored over a first predetermined extended periodof time; retrieving from the database a second set of heart ratevariability measures stored over a second predetermined extended periodof time subsequent to the first extended period of time; and computing ameasure of fitness of the user based on the first set of retrieved heartrate variability measures and the second set of retrieved heart ratevariability measures.
 38. The computer process of claim 37, furthercomprising managing presenting successive instances of the measure offitness to the user on a regular basis.
 39. The computer process ofclaim 37, further comprising: managing presenting to the user a firstmeasure of fitness computed for a first period of time; and managingpresenting to the user, at substantially the same time, a second measureof fitness computed for a second period of time substantially differentfrom the first period of time.
 40. The computer process of claim 27,further comprising: managing presenting to the user a set of personalprofile questions including at least age and sex; managing inputtingfrom the user answers to the questions; managing capturing an initialheart rate variability measure; comparing the initial heart ratevariability measure to heart rate variability norms appropriate to theage and sex of the user; managing presenting to the user the result ofthe comparison; generating scores based on the deviation of the answersfrom corresponding values recommended by at least one advisory body oncardiovascular health; and managing presenting based on the scores atleast one recommendation for improving the heart rate variabilitymeasure of the user.
 41. The computer process of claim 27, furthercomprising: managing measuring resting heart rate and heart ratevariability of the user a number of times sufficient to compute a validnormal value of resting heart rate and a valid normal value of heartrate variability before the user engages in physical training; computingthe valid normal value of resting heart rate and the valid normal valueof heart rate variability of the user; managing measuring resting heartrate after the user engages in physical training; managing presenting atleast one recommendation to the user relevant to parasympatheticovertraining if the resting heart rate of the user measured aftertraining is less than the normal resting heart rate of the user;managing measuring heart rate variability after the user engages inphysical training if the resting heart rate of the user after physicaltraining is not less than the normal resting heart rate of the user;managing presenting at least one recommendation to the user relevant tosympathetic overtraining if the heart rate variability of the usermeasured after training is less than the normal resting heart ratevariability of the user and the resting heart rate of the user measuredafter training is greater than the normal resting heart rate of theuser; managing presenting at least one recommendation to the userrelevant to normal training if the heart rate variability of the usermeasured after training is not less than the normal resting heart ratevariability of the user; and managing presenting at least onerecommendation to the user relevant to normal training if the heart ratevariability of the user measured after training is less than the normalheart rate variability of the user and the resting heart rate of theuser measured after training is not greater than the normal restingheart rate of the user.
 42. The computer process of claim 27, furthercomprising: managing determining if the user desires recommendationsrelevant to heart failure patients; managing instructing the user to sitor lie down; wherein instructing the user when to breathe in and breatheout comprises instructing the user to breathe in more quickly andbreathe out more quickly than would be the instruction to a user whodoes not suffer from heart failure; computing the number of excludedbeats over a predetermined period of time; managing terminating theheart rate variability measurement if the number of excluded beatsexceeds a predetermined threshold; and managing presenting at least oneexercise recommendation appropriate to the user based on the measure ofheart rate variability if the number of excluded beats does not exceedthe predetermined threshold.
 43. The computer process of claim 27,further comprising: calculating heart rate from the processed heart beatintervals after excluding irregular heart beat intervals; calculating aParasympathetic Withdrawal Index using the current value of the heartrate and a prior value of the heart rate and the current value of theheart rate variability measure and a prior value of the heart ratevariability measure; and managing generating an alarm to the user if theParasympathetic Withdrawal Index is less than a predetermined threshold.44. The computer process of claim 27, further comprising: calculating anaverage comprising the current measure of heart rate variability andprevious measures of heart rate variability; and managing presenting tothe user the average.
 45. The computer process of claim 44, wherein theaverage comprises a rolling average.
 46. The computer process of claim45, wherein the rolling average comprises a seven day average.
 47. Thecomputer process of claim 44, wherein the measure of heart ratevariability comprises the natural logarithm of RMSSD.
 48. The computerprocess of claim 27, further comprising presenting to the user one ormore recommendations on type or intensity of training to perform. 49.The computer process of claim 48, wherein the recommendations arepresented as text on a device operated by the user.
 50. A computersoftware product embodied in a non-transitory computer-readable physicalmedium comprising coded instructions for executing a computer process ina digital processor, which computer process generates a measure of heartrate variability, the computer process comprising: inputting processedheart beat intervals; wherein the processed heart beat intervals areoutput by a signal processing means and an input of the signalprocessing means comprises a signal received by a receiving means from asensor responsive to the user's heartbeat; calculating a measure ofheart rate variability of the user from the processed heart beatintervals; excluding irregular heart beat intervals from the calculationof heart rate variability; calculating the number of excluded heartbeats over a predetermined period of time; and managing presenting tothe user the number of excluded heart beats.
 51. The computer process ofclaim 50, wherein the predetermined period of time comprises one hour.52. The computer process of claim 50, further comprising: maintaining arunning count of the number of consecutive heart beats that are excludedwithin the predetermined period of time; and presenting to the user anindication if the running count is greater than or equal to three.